Artificial intelligence for microgrids design, control, and maintenance: A comprehensive review and prospects

The rising use of renewable energy sources in today's landscape underscores the growing significance of microgrids. As the deployment of microgrids continues to grow, there is a pressing need to develop advanced control and management strategies to overcome the challenges associated with their integration and operation. Notably, Artificial Intelligence (AI) is a rapidly developing field that is well-positioned to effectively address these challenges. This paper begins by exploring the fundamentals of microgrids, emphasizing their structure, components, and control aspects. After introducing essential AI techniques, it analyzes their role in various microgrid domains, such as design, control, and maintenance. (1) In the design realm, the focus is on optimal sizing. (2) In the realm of control, a hierarchical framework comprising primary, secondary, and tertiary levels is introduced. Within the tertiary control level, the focus is directed toward energy management systems. (3) Maintenance involves activities such as condition monitoring, diagnosis, and prognosis. In this context, focusing on power quality (PQ) issues underscores the role of AI in fault detection and classification as an integral part of the diagnostic process. Finally, the paper explores cutting-edge technologies and future advancements in microgrids. It highlights the integration of IoT for improved connectivity and data exchange, emphasizes the role of federated learning and blockchain in bolstering cybersecurity, and explores the transformative potential of digital twins in optimizing microgrid operations.